TY - GEN
T1 - Deep Learning-Based Multi-tasking System for Diabetic Retinopathy in UW-OCTA Images
AU - Cho, Jungrae
AU - Shon, Byungeun
AU - Jeong, Sungmoon
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Diabetic retinopathy causes various abnormality in retinal vessels. In addition, Detection and identification of vessel anomaly are challenging due to nature of complexity in retinal vessels. UW-OCTA provides high-resolution image of those vessels to diagnose lesions of vessels. However, the image suffers noise of image. We here propose a deep learning-based multi-tasking systems for DR in UW-OCTA images to deal with diagnosis and checking image quality. We segment three kinds of retinal lesions with data-adaptive U-Net architectures, i.e. nnUNet, grading images on image quality and DR severity grading by soft-voting outputs of fine-tuned multiple convolutional neural networks. For three tasks, we achieve Dice similarity coefficient of 0.5292, quadratic weighted Kappa of 0.7246, and 0.7157 for segmentation, image quality assessment, and grading DR for test set of DRAC2022 challenge. The performance of our proposed approach demonstrates that task-adaptive U-Net planning and soft ensemble of CNNs can provide enhancement of the performance of single baseline models for diagnosis and screening of UW-OCTA images.
AB - Diabetic retinopathy causes various abnormality in retinal vessels. In addition, Detection and identification of vessel anomaly are challenging due to nature of complexity in retinal vessels. UW-OCTA provides high-resolution image of those vessels to diagnose lesions of vessels. However, the image suffers noise of image. We here propose a deep learning-based multi-tasking systems for DR in UW-OCTA images to deal with diagnosis and checking image quality. We segment three kinds of retinal lesions with data-adaptive U-Net architectures, i.e. nnUNet, grading images on image quality and DR severity grading by soft-voting outputs of fine-tuned multiple convolutional neural networks. For three tasks, we achieve Dice similarity coefficient of 0.5292, quadratic weighted Kappa of 0.7246, and 0.7157 for segmentation, image quality assessment, and grading DR for test set of DRAC2022 challenge. The performance of our proposed approach demonstrates that task-adaptive U-Net planning and soft ensemble of CNNs can provide enhancement of the performance of single baseline models for diagnosis and screening of UW-OCTA images.
KW - diabetic retinopathy
KW - ensemble
KW - semantic segmentation
KW - SS-OCTA
KW - UW-OCTA
UR - http://www.scopus.com/inward/record.url?scp=85173566975&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33658-4_9
DO - 10.1007/978-3-031-33658-4_9
M3 - Conference contribution
AN - SCOPUS:85173566975
SN - 9783031336577
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 88
EP - 96
BT - Mitosis Domain Generalization and Diabetic Retinopathy Analysis - MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Sheng, Bin
A2 - Aubreville, Marc
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -
